Table REF  summarizes quantitative results on the PASCAL-5\(^{i}\)  [1]}. We denote the type of backbone feature extractor and number of parameters for comparison. For PASCAL-5\(^{i}\) , we tested with two backbone networks, ResNet50 and ResNet101 [2]}. The proposed method outperforms other methods for almost all the folds with respect to both mIoU and FB-IoU. Consistent with this, VAT also attains state-of-the-art performance on COCO-20\(^{i}\)  [3]} for both 1-shot and 5-shot. Interestingly, for the most recently introduced dataset deliberately created for few-shot segmentation task, FSS-1000 [4]}, VAT outperforms HSNet [5]} and FSOT [6]} by a large margin, almost 4\(\%\)  increase in mIoU compared to HSNet with ResNet50. Overall, VAT sets a new state-of-the-art for all the benchmarks and this is confirmed at both Table REF  and Table REF . The qualitative results are shown in Fig. REF .
